Tutorial stats 011422: Difference between revisions
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=== Install Afni on computer: === |
=== Install Afni on computer: === |
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https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html |
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html |
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=== Statistical Processing in AFNI === |
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#! /bin/bash |
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# First things first: Good practice to convert .nii files to afni .HEAD .BRIK format: 3dcopy mydataset.nii myafnidataset |
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# Here the data have been converted to common space (+tlrc) but if not can be done in afni with adwarp |
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# Example: One Sample T-test on alpha band log ratio of face power and shape power |
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# Why log-ratio? log(faces/shapes) = log(faces) - log(shapes) ; log attentuates outliers , normalizes distribution. |
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# First make a list of subjects for test: |
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ls *_7_13_*lograt*.HEAD > alpha_list |
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# Then run one sample T-test |
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3dttest++ -setA `cat alpha_list` -prefix alpha_ttest |
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# Optional: add effect size to output |
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3dMean -prefix alpha_mean `cat alpha_list` |
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3dMean -sd -prefix alpha_std `cat alpha_list` |
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3dcalc -prefix alpha_effectsize -a alpha_mean+tlrc -b alpha_std+tlrc -expr 'a/b' |
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3dbucket -prefix alpha_ttest_effectsize alpha_ttest+tlrc alpha_effectsize+tlrc |
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3drefit -sublabel 2 "effectsize" alpha_ttest_effectsize+tlrc |
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# Could do a paired T-test between faces and shapes instead of log-ratio |
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ls *_7_13_*shape*.HEAD > alpha_shape_list |
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ls *_7_13_*face*.HEAD > alpha_face_list |
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3dttest++ -setA `cat alpha_face_list` -setB `cat alpha_shape_list` -paired -prefix alpha_paired_ttest |
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# Can add covariates to T-tests. |
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# Can do mixed-effects ANOVAs and other fancier stuff |
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== MNE Python == |
== MNE Python == |
Revision as of 13:29, 24 January 2022
!!UNDER CONSTRUCTION!!
Afni Prep
Install Afni on computer:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html
Statistical Processing in AFNI
#! /bin/bash # First things first: Good practice to convert .nii files to afni .HEAD .BRIK format: 3dcopy mydataset.nii myafnidataset # Here the data have been converted to common space (+tlrc) but if not can be done in afni with adwarp # Example: One Sample T-test on alpha band log ratio of face power and shape power # Why log-ratio? log(faces/shapes) = log(faces) - log(shapes) ; log attentuates outliers , normalizes distribution. # First make a list of subjects for test: ls *_7_13_*lograt*.HEAD > alpha_list # Then run one sample T-test 3dttest++ -setA `cat alpha_list` -prefix alpha_ttest # Optional: add effect size to output 3dMean -prefix alpha_mean `cat alpha_list` 3dMean -sd -prefix alpha_std `cat alpha_list` 3dcalc -prefix alpha_effectsize -a alpha_mean+tlrc -b alpha_std+tlrc -expr 'a/b' 3dbucket -prefix alpha_ttest_effectsize alpha_ttest+tlrc alpha_effectsize+tlrc 3drefit -sublabel 2 "effectsize" alpha_ttest_effectsize+tlrc # Could do a paired T-test between faces and shapes instead of log-ratio ls *_7_13_*shape*.HEAD > alpha_shape_list ls *_7_13_*face*.HEAD > alpha_face_list 3dttest++ -setA `cat alpha_face_list` -setB `cat alpha_shape_list` -paired -prefix alpha_paired_ttest # Can add covariates to T-tests. # Can do mixed-effects ANOVAs and other fancier stuff
MNE Python
Install MNE Python
conda install mamba -y mamba create -n tutorial_stats conda-forge::mne conda-forge:datalad -y conda activate tutorial_stats
Download the data
git clone ------- data repo cd ---- datalad get ./*
Additional MEG Stats Links
https://mne.tools/stable/auto_tutorials/stats-sensor-space/10_background_stats.html https://eelbrain.readthedocs.io/en/stable/getting_started.html https://neuroimage.usc.edu/brainstorm/Tutorials/Statistics https://www.fieldtriptoolbox.org/tutorial/eventrelatedstatistics/